From 9e8a12af40bb20fb5c4fb6b51777c94732e18b98 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 09 Nov 2015 21:38:18 +0000
Subject: [PATCH] extraction conv layers
---
src/network.c | 34 +++++++++++++++++++---------------
1 files changed, 19 insertions(+), 15 deletions(-)
diff --git a/src/network.c b/src/network.c
index d823c15..9bcb264 100644
--- a/src/network.c
+++ b/src/network.c
@@ -11,7 +11,6 @@
#include "convolutional_layer.h"
#include "deconvolutional_layer.h"
#include "detection_layer.h"
-#include "region_layer.h"
#include "normalization_layer.h"
#include "maxpool_layer.h"
#include "avgpool_layer.h"
@@ -29,15 +28,26 @@
float get_current_rate(network net)
{
int batch_num = get_current_batch(net);
+ int i;
+ float rate;
switch (net.policy) {
case CONSTANT:
return net.learning_rate;
case STEP:
- return net.learning_rate * pow(net.gamma, batch_num/net.step);
+ return net.learning_rate * pow(net.scale, batch_num/net.step);
+ case STEPS:
+ rate = net.learning_rate;
+ for(i = 0; i < net.num_steps; ++i){
+ if(net.steps[i] > batch_num) return rate;
+ rate *= net.scales[i];
+ }
+ return rate;
case EXP:
return net.learning_rate * pow(net.gamma, batch_num);
case POLY:
return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ case SIG:
+ return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
default:
fprintf(stderr, "Policy is weird!\n");
return net.learning_rate;
@@ -61,8 +71,6 @@
return "softmax";
case DETECTION:
return "detection";
- case REGION:
- return "region";
case DROPOUT:
return "dropout";
case CROP:
@@ -108,8 +116,6 @@
forward_normalization_layer(l, state);
} else if(l.type == DETECTION){
forward_detection_layer(l, state);
- } else if(l.type == REGION){
- forward_region_layer(l, state);
} else if(l.type == CONNECTED){
forward_connected_layer(l, state);
} else if(l.type == CROP){
@@ -169,10 +175,6 @@
sum += net.layers[i].cost[0];
++count;
}
- if(net.layers[i].type == REGION){
- sum += net.layers[i].cost[0];
- ++count;
- }
}
return sum/count;
}
@@ -213,8 +215,6 @@
backward_dropout_layer(l, state);
} else if(l.type == DETECTION){
backward_detection_layer(l, state);
- } else if(l.type == REGION){
- backward_region_layer(l, state);
} else if(l.type == SOFTMAX){
if(i != 0) backward_softmax_layer(l, state);
} else if(l.type == CONNECTED){
@@ -319,6 +319,7 @@
//if(w == net->w && h == net->h) return 0;
net->w = w;
net->h = h;
+ int inputs = 0;
//fprintf(stderr, "Resizing to %d x %d...", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
@@ -332,9 +333,12 @@
break;
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
+ }else if(l.type == COST){
+ resize_cost_layer(&l, inputs);
}else{
error("Cannot resize this type of layer");
}
+ inputs = l.outputs;
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
@@ -525,12 +529,12 @@
return acc;
}
-float *network_accuracies(network net, data d)
+float *network_accuracies(network net, data d, int n)
{
static float acc[2];
matrix guess = network_predict_data(net, d);
- acc[0] = matrix_topk_accuracy(d.y, guess,1);
- acc[1] = matrix_topk_accuracy(d.y, guess,5);
+ acc[0] = matrix_topk_accuracy(d.y, guess, 1);
+ acc[1] = matrix_topk_accuracy(d.y, guess, n);
free_matrix(guess);
return acc;
}
--
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